• The Journal of Information Systems
• /
• v.27 no.3
• /
• pp.123-140
• /
• 2018

Purpose The purpose of this study is to explore the optimal trading frequency which is useful for stock price prediction by using deep learning for charting image data. We also want to identify the appropriate time for accurate forecasting of stock price when performing pattern analysis. Design/methodology/approach In order to find the optimal trading frequency patterns and forecast timings, this study is performed as follows. First, stock price data is collected using OpenAPI provided by Daishin Securities, and candle chart images are created by data frequency and forecasting time. Second, the patterns are generated by the charting images and the learning is performed using the CNN. Finally, we find the optimal trading frequency patterns and forecasting timings. Findings According to the experiment results, this study confirmed that when the 10 minute frequency data is judged to be a decline pattern at previous 1 tick, the accuracy of predicting the market frequency pattern at which the market decreasing is 76%, which is determined by the optimal frequency pattern. In addition, we confirmed that forecasting of the sales frequency pattern at previous 1 tick shows higher accuracy than previous 2 tick and 3 tick.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.6 no.2
• /
• pp.339-345
• /
• 2002

Extracting of car licens plate os important for identifying the car. Since there are some problems such as poor ambient lighting problem, bad weather problem and so on, the car images are distorted and the car license plate is difficult to be extracted. This paper proposes a method of extracting car license plate using motor vehicle regulation. In this method, some features of car license plate according to motor vehicle regulation such as color information, shape are applied to determine the candidate of car license plates. For the result of recognition by neural network, the candidate which has characters and numbers patterns according to motor vehicle regulation is certified as license-plate region. The results of the experiments with 70 samples of real car images shoe the performance of car license-plate extraction by 84.29%, and the recognition rate is 80.81%.

• Journal of information and communication convergence engineering
• /
• v.1 no.4
• /
• pp.223-228
• /
• 2003

In this paper, we introduce a hyper-chaos secure communication method using hyper-chaos circuit onsist of State-Controlled Cellular Neural Network SC-CNN). We make a hyper-chaos circuit using SC-CNN with the n-double scroll or Chua's oscillator. A hyper-chaos circuit is created by applying identical n-double scroll or non-identical n-double scroll and Chua's oscillator with weak coupled method to each cell. Hyper-chaos ynchronization was achieved using GS (Generalized Synchronization) method between the transmitter and receiver about each state variable in the SC-CNN. In order to secure communication, we have synthesizing the desired information with a hyper-chaos circuit by adding the information signal to the hyper-chaos signal using the SC-CNN in the transmitter. And then, transmitting the synthesized signal to the ideal channel, we confirm secure communication by separating the information signal and the hyper-chaos signal in the receiver.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.11
• /
• pp.2505-2511
• /
• 2013

Many studies in detection and classification of the targets in the underwater environments have been conducted for military purposes, as well as for non-military purpose. Due to the complicated characteristics of underwater acoustic signal reflecting multipath environments and spatio-temporal varying characteristics, active sonar target classification technique has been considered as a difficult technique. And it has difficulties in collecting actual underwater data. In this paper, we synthesized active target echoes based on ray tracing algorithm using target model having 3-dimensional highlight distribution. Then, Fractional Fourier transform was applied to synthesized target echoes to extract feature vector. Recognition experiment was performed using neural network classifier.

• KIEAE Journal
• /
• v.15 no.4
• /
• pp.105-110
• /
• 2015

Purpose: This study aimed at finding the optimal input variables of the artificial neural network-based predictive model for the optimal controls of the indoor temperature environment. By applying the optimal input variables to the predictive model, the required time for restoring the current indoor temperature during the setback period to the normal setpoint temperature can be more precisely calculated for the cooling season. The precise prediction results will support the advanced operation of the cooling system to condition the indoor temperature comfortably in a more energy-efficient manner. Method: Two major steps employing the numerical computer simulation method were conducted for developing an ANN model and finding the optimal input variables. In the first process, the initial ANN model was intuitively determined to have input neurons that seemed to have a relationship with the output neuron. The second process was conducted for finding the statistical relationship between the initial input variables and output variable. Result: Based on the statistical analysis, the optimal input variables were determined.

• Proceedings of the KIEE Conference
• /
• 2001.11c
• /
• pp.431-434
• /
• 2001

단층 퍼셉트론이 처음 개발되었을 때, 간단한 패턴을 인식하는 학습 기능을 가지고 있기 장점 때문에 학자들의 관심을 끌었다. 단층 퍼셉트론은 한 개의 소자를 이용해서 이진 논리를 가중치(weight)의 변경만으로 모두 표현할 수 있는 장점 때문에 영상처리, 패턴인식, 장면인식 등에 이용되어 왔다. 최근에, 역전파학습(Back-Propagation Learning)알고리즘이 이진 공간내의 매핑 문제에 적용되고 있다. 그러나, 역전파 학습알고리즘은 연속공간 내에서 긴 학습시간과 비효율적인 수행의 문제를 가지고 있다. 일반적으로 역전파 학습 알고리즘은 간단한 이진 공간에서 매핑하기 위해서 많은 반복과정을 요구한다. 역전파 학습 알고리즘에서는 은닉층의 뉴런의 수는 주어진 문제를 해결하기 위해서 우선순위(prior)를 알지 못하기 때문에 입력층과 출력층내의 뉴런의 수에 의존한다. 따라서, 3층 신경회로망의 적용에 있어 가장 중요한 문제중의 하나는 은닉층내의 필요한 뉴런수를 결정하는 것이고, 회로망 합성과 가중치 결정에 대한 적절한 방법을 찾지 못해 실제로 그 사용 영역이 한정되어 있었다. 본 논문에서는 패턴 분류를 위한 새로운 학습방법을 제시한다. 훈련입력의 기하학적인 분석에 기반을 둔 이진 신경회로망내의 은닉층내의 뉴런의 수를 자동적으로 결정할 수 있는 NETLA(Newly Expand and Truncate Learning Algorithm)라 불리우는 기하학적 학습알고리즘을 제시하고, 시뮬레이션을 통하여, 제안한 알고리즘의 우수성을 증명한다.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2004.11a
• /
• pp.653-657
• /
• 2004

In this study, PD(partial discharge) signals which occurrs at stator coil of traction Motor are acquired. these data are used for classifying the PD sources. W(Neural Network) has recently applied to classify the PB pattern. The PD data are used for the learning process to classify PD sources. The PD data come from normal specimen and defective specimens such as internal void discharges, slot discharges and surface discharges. PD distribution parameters are calculated from a set of the data, which is used to realize diagnostic algorithm. NN which applies distribution parameters is useful to classify the PD patterns of defective sources generating in stator coil of traction motor.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.5
• /
• pp.917-926
• /
• 2010

It is known that bagging and boosting techniques improve the performance in classification problem. A number of researchers have proved the high performance of bagging and boosting through experiments for categorical response but not for continuous response. We study whether bagging and boosting improve data mining methods for continuous responses such as linear regression, decision tree, neural network through bagging and boosting. The analysis of eight real data sets prove the high performance of bagging and boosting empirically.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2004.07b
• /
• pp.1117-1120
• /
• 2004

경제적인 공정분석과 최적화를 위해서는 컴퓨터를 이용한 플라즈마 예측모델이 요구되고 있다. 본 연구에서는 일반화된 회귀 신경망 (GRNN)을 이용하여 플라즈마 증착공정 모델을 개발한다. GRNN의 예측성능은 패턴층 뉴런의 가우시안 함수를 구성하는 학습인자, 즉 spread에 의존한다. 종래의 모델에서는 모든 가우시안 함수의 spread가 동일한 값에서 최적화되었으며, 이로 인해 모델의 예측성능을 향상시키는 데에는 한계가 있었다. 본 연구에서는 유전자 알고리즘 (GA)를 이용하여 다변수 spread를 최적화하는 기법을 개발하였으며, 그 성능을 PECVD 공정에 의해 증착된 SiN 박막의 증착률에 적용하여 평가하였다. $2^{6-1}$ 부분인자 실험계획법에 의해 수집된 데이터를 이용하여 신경망을 학습하였고, 모델적합성 점검을 위해 별도의 12번의 실험을 수행하였다. 가우시안 함수의 spread는 0.2에서 2.0까지 0.2간격으로 증가시켰으며, 최적화한 GA-GRNN모델의 예측성능은 6.6 ${\AA}/min$이었다. 이는 종래의 방식으로 최적화한 모델의 예측성능 (13.5 ${\AA}/min$)과 비교하여 50.7% 향상된 예측성능이며, 이러한 향상은 제안한 GA-GRNN 모델이 플라즈마 공정 모델의 예측성능을 증진하는데 매우 효과적임을 보여준다.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.14 no.3
• /
• pp.356-363
• /
• 2014

In this paper, we propose a new memristor-based crossbar array architecture, where a single memristor array and constant-term circuit are used to represent both plus-polarity and minus-polarity matrices. This is different from the previous crossbar array architecture which has two memristor arrays to represent plus-polarity and minus-polarity connection matrices, respectively. The proposed crossbar architecture is tested and verified to have the same performance with the previous crossbar architecture for applications of character recognition. For areal density, however, the proposed crossbar architecture is twice better than the previous architecture, because only single memristor array is used instead of two crossbar arrays. Moreover, the power consumption of the proposed architecture can be smaller by 48% than the previous one because the number of memristors in the proposed crossbar architecture is reduced to half compared to the previous crossbar architecture. From the high areal density and high energy efficiency, we can know that this newly proposed crossbar array architecture is very suitable to various applications of analog neuromorphic computing that demand high areal density and low energy consumption.